Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

Abstract

The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer’s, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

Genomics and Drug Discovery Strategies: The Role of Natural Compounds and Its Receptor in Alzheimer’s Disease

“…As an outcome of docking, it was revealed that the receptors and ligands showed minimum binding energy (-ΔG), leading to the prediction of probable drug ligands. Further, the docking score was critically analyzed and visualized using Biovia Discovery Studio [22], followed by molecular dynamics simulation (MD) with iMOD [23].…”

Special Issue: “Bioinformatics and Omics Tools”

“…Using gene expression data and bioinformatics tools, the researchers identified common genes between P. gingivalis infection and AD and performed in silico molecular modeling to identify potential drug candidates targeting these genes. This study highlights the potential for integrating omics datasets and computational approaches to elucidate complex disease associations and discover novel therapeutic opportunities [3].…”